Students’ perceptions of teaching technologies, application of technologies, and academic performance

This study examined business students’ perceptions of four objectives (i.e., Enjoyment, Learning, Motivation, and Career Application) across five teaching technologies (i.e., Projector, PowerPoint, Video, the Internet, and Lecture), business professors’ effective application of technologies, and students’ academic performance. We collected data from 215 students at a regional state university in the USA. We developed Students’ Perceptions of Technology Scale, SPOTS, specifically for the present study, used the most rigorous criteria, and investigated reliability, convergent and discriminant validity, exploratory and confirmatory factor analysis, and measurement invariance of this scale. Mean scores revealed that Video conveyed the highest amount of Enjoyment. PowerPoint provided the highest amount of Learning and Motivation. The Internet provided the highest Career Application for future jobs. Younger students preferred Video, whereas older students favored Lecture. Regression results showed that the use of Video for Learning, Projector and Lecture for Enjoyment, PowerPoint for career and Motivation, and the Internet for Learning contributed to professors’ teaching effectiveness. Students’ high ratings for professors’ effective use of the Lecture method and low expectation for the use of a Projector predicted their self-reported GPA (academic performance). Professors may use a different mix of technologies in the classroom and use them creatively in order to promote the most Learning for students and satisfy students’ Learning needs and objectives.     

An e-commerce performance assessment model: Its development and an initial test on e-commerce applications in the retail sector of China

We developed a performance assessment model for e-commerce; it included indicators, indicator weights, and evaluation methods. The model has seven methods of assessing e-commerce performance and uses four criteria to compare and select the appropriate one for a particular situation. This model was tested in the retail sector of China. According to the data collected from 70 Chinese retailers, 16 indicators of e-commerce performance consisted of four constructs: marketing and sales, customer service, supply chain efficiency, and financial performance. The indicators for those constructs provide a comprehensive measurement of performance. Then weights were assigned for each indicator using a majority aggregation method. Comparison of the results from seven evaluation methods showed that discordance analysis and simple additive weighting were the best evaluation methods for the enterprises we had sampled.     

Modeling enterprise service-oriented architectural styles

Modern enterprise consists of complicate business processes and systems. The Enterprise Service-Oriented Architecture (ESOA) becomes an important architectural style that defines the principles for coping with the complexity of designing and implementing business systems. This paper classifies ESOA styles to six substyles and proposes a generic and abstract model for ESOA styles. The model consists of seven sets: services, service consumers, service data, infrastructure, processes, management and quality attributes. This paper formally defines each set and their relationships in ESOA style model, and discusses the roles of these sets. The model can be applied to specify various ESOA styles. As case studies, the definition of instance of ESOA style is applied to analyze and evaluate a Java component-based ESOA-style architecture and several other ESOA-style architectures. Finally, this paper concludes by comparing the proposed model with related ESOA models.     

Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach

Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on the discretization of them prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the models. However, the discretization can be problematic due to its textit{ad hoc} nature and the decreased statistical power to detect the correct classes in the resulting procedure. We introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets.     

Two-Dimensional Multilabel Active Learning with an Efficient Online Adaptation Model for Image Classification

Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site—Corbis.     

Design and implementation of an agent-based collaborative product design system

The evolution of computer science and technology has brought new opportunities for multidisciplinary designers and engineers to collaborate with each other in a concurrent and coordinated manner. The development of computational agents with unified data structures and software protocols contributes to the establishment of a new way of working in collaborative design, which is increasingly becoming an international practice. In this paper, based on the analysis of the dynamic nature of collaborative design process, a new framework for collaborative design is described. This framework adopts an agent-based approach and relocates designers, managers, systems, and the supporting agents in a unified knowledge representation scheme for product design. In order to model the constantly evolving design process and the rationales resulted from design collaboration, a Collaborative Product Data Model (CPDM) and a constraint-based Collaborative Design Process Model (CDPM) are proposed to facilitate the management and coordination of the collaborative design process as well as design knowledge management. A prototype system of the proposed framework is implemented and its feasibility is evaluated using a real design scenario whose objective is designing a set of dining table and chairs.     

Modeling Default Data Via an Interactive Hidden Markov Model

In this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a “dynamic” version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM.     

Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification

A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.     

UV surface exposure for low temperature hydrophilic silicon direct bonding

The ultraviolet (UV) surface exposure is applied in hydrophilic silicon direct bonding as an additional treatment following the traditional wet chemical activation processes. Infrared inspection and tensile strength test are then performed. It is found that improvement in bond-rate, interface uniformity as well as high tensile strength is obtained. The mechanism of UV exposure including surface cleaning and surface modification is discussed in detail. Mixed spectrum emitted from the UV source comprising 254 and 185 nm wavelength efficiently removes the resided organic contaminations on silicon surface by photosensitization and oxidation effects. Besides, considerable hydroxyl incorporations are generated by UV reacting with surface water molecular and silicon dioxide. Thus, appropriate UV exposure will render the silicon surface more hydrophilic and improve bondability. It is concluded that as an additional cleaning and modification treatment, UV exposure is effective and promising to enhanced bond quality in low temperature hydrophilic silicon direct bonding.